Constrained Clustering with Interactive Similarity Learning
Masayuki Okabe, Seiji Yamada · SCIS & ISIS SCIS & ISIS 2010 · 2010
This paper describes an interactive tool for constrained clustering that helps users to select effective constraints efficiently during the constrained clustering process. This tool has some functions such as 2-D visual arrangement of a data set and constraint assignment by mouse manipulation. Moreover, it can execute distance metric learning and k-medoids clustering. In this paper, we show the overview of the tool and how it works, especially in the functions of display arrangement by multidimensional scaling and incremental distance metric learning. Eventually we show a preliminary experiment in which human heuristics found through our GUI improve the clustering.